Recent advances in large language models (LLMs) have opened new possibilities for artificial intelligence applications in finance. In this paper, we provide a practical survey focused on two key aspects of utilizing LLMs for financial tasks: existing solutions and guidance for adoption. First, we review current approaches employing LLMs in finance, including leveraging pretrained models via zero-shot or few-shot learning, fine-tuning on domain-specific data, and training custom LLMs from scratch. We summarize key models and evaluate their performance improvements on financial natural language processing tasks. Second, we propose a decision framework to guide financial professionals in selecting the appropriate LLM solution based on their use case constraints around data, compute, and performance needs. The framework provides a pathway from lightweight experimentation to heavy investment in customized LLMs. Lastly, we discuss limitations and challenges around leveraging LLMs in financial applications. Overall, this survey aims to synthesize the state-of-the-art and provide a roadmap for responsibly applying LLMs to advance financial AI.

翻译：近年来，大型语言模型（LLMs）的进展为人工智能在金融领域的应用开辟了新的可能性。本文提供了一份实用性综述，重点关注利用LLMs完成金融任务的两个关键方面：现有解决方案与采用指南。首先，我们回顾了当前在金融领域应用LLMs的方法，包括通过零样本或小样本学习利用预训练模型、在特定领域数据上进行微调，以及从头开始训练定制化的LLMs。我们总结了关键模型，并评估了它们在金融自然语言处理任务上的性能提升。其次，我们提出了一个决策框架，以指导金融从业者根据其用例在数据、计算和性能需求方面的约束，选择合适的LLM解决方案。该框架提供了一条从轻量级实验到对定制化LLMs进行重大投入的路径。最后，我们讨论了在金融应用中利用LLMs的局限性与挑战。总体而言，本综述旨在综合该领域的最新进展，并为负责任地应用LLMs以推动金融人工智能的发展提供路线图。